This application relates to devices and techniques that use medical imaging technologies.
Magnetoencephalography (MEG) is a functional imaging modality that directly detects neuronal activity. Because a number of different source configurations can generate the same MEG signal, assumptions are made about the nature of the sources (source models) to uniquely localize them. Conventional MEG source-modeling methods require a priori information when localizing highly-correlated networks from noisy MEG data.
The beamformer methodology is a spatial-filtering approach where the MEG sensor signal is filtered by different beams based on lead-field vectors corresponding to specific source-grid points. Each of these operations generates a pseudo-Z-statistic, which can be maximized to find the most highly-contributing source-grid dipoles. The beamformer method has low computational cost, although the orientation angle of each dipole must be optimized. The beamformer approach generally works well for MEG data with a low SNR. However, the conventional beamformer suppresses source-power estimates from source-grid dipoles that have highly correlated time-courses, as the method assumes that source time-courses from different generators are uncorrelated. Variants of the beamformer method, including the coherently combining signal-to-interference plus noise ratio (CC-SINR) beamformer and the constant modulus algorithm (CMA) beamformer, address reconstruction of correlated sources, but have been met with moderate success. Likewise, the coherent source suppression model (CCSM) and the independently developed nulling beamformer (NB) accurately reconstruct correlated sources but require a priori information of interfering source locations. Furthermore, entire unknown pathways of neural activity cannot be easily identified since correlated sources are suppressed to reconstruct a single source of interest.